Abstract: Systems and methods for implementing an adaptive therapy workflow that minimizes time needed to create a session patient model, select an appropriate plan for the treatment session, and treat the patient.

Abstract: A radiation treatment plan three-dimensional dose prediction machine learning model is trained using a training corpus that includes a plurality of radiation treatment plans that are not specific to a particular patient and wherein the training corpus includes some, but not all, possible patient volumes of interest. Information regarding the patient (including information regarding at least one volume of interest for the patient that was not represented in the training corpus) is input to the radiation treatment plan three-dimensional dose prediction machine model. The latter generates predicted three-dimensional dose distributions that include a predicted three-dimensional dose distribution for the at least one volume of interest that was not represented in the training corpus.

Abstract: Patient information for a particular patient, radiation treatment information, and information regarding a particular radiation treatment platform that includes at least one multi-leaf collimator are each accessed. These teachings then provide for optimizing a radiation treatment plan to dose at least one treatment volume in the particular patient using the particular radiation treatment platform as a function of the patient information, the radiation treatment information, and the information regarding the particular radiation treatment platform, wherein the optimizing does not include optimizing movement of any leaves of the multi-leaf collimator.

Abstract: Systems and methods for implementing an adaptive therapy workflow that minimizes time needed to create a session patient model, select an appropriate plan for the treatment session, and treat the patient.

Abstract: Systems and methods for the automatic generation and optimization of radiation therapy treatment plans, and systems and methods for the automatic generation and optimization of an adapted plan in an adaptive radiation therapy workflow.

Abstract: Systems and methods for implementing an adaptive therapy workflow that minimizes time needed to create a session patient model, select an appropriate plan for the treatment session, and treat the patient.

Abstract: Systems and methods for the automatic generation and optimization of radiation therapy treatment plans, and systems and methods for the automatic generation and optimization of an adapted plan in an adaptive radiation therapy workflow.

Abstract: A prescribing user can designate and prioritize clinical goals that can, in turn, serve as the basis for optimization objectives to guide the development of a radiation treatment plan. The prescribing user can then alter that prioritization of one or more clinical goals and view information regarding changes to fluence-based dose distributions that occur in response to those changes to relative prioritization amongst the clinical goals to thereby understand dosing tradeoffs that correspond to prioritization amongst the clinical goals.

Abstract: Systems and methods for implementing an adaptive therapy workflow that minimizes time needed to create a session patient model, select an appropriate plan for the treatment session, and treat the patient.

Abstract: A control circuit presents to a user an automatic radiation treatment plan optimizer that provides pre-existing accuracy input parameters and that includes an opportunity to select a stereotactic body radiation treatment planning mode in addition to other treatment planning modes. In response to a user selecting the stereotactic body radiation treatment planning mode, the control circuit automatically overrides at least one of the pre-existing accuracy input parameters when automatically optimizing a radiation treatment plan.

Abstract: Patient information for a particular patient, radiation treatment information, and information regarding a particular radiation treatment platform that includes at least one multi-leaf collimator are each accessed. These teachings then provide for optimizing a radiation treatment plan to dose at least one treatment volume in the particular patient using the particular radiation treatment platform as a function of the patient information, the radiation treatment information, and the information regarding the particular radiation treatment platform, wherein the optimizing does not include optimizing movement of any leaves of the multi-leaf collimator.

Abstract: Systems and methods for implementing an adaptive therapy workflow that minimizes time needed to create a session patient model, select an appropriate plan for the treatment session, and treat the patient.

Abstract: A control circuit accesses historical information regarding previously optimized radiation treatment plans for different patients and processes that information to determine the relative importance of different clinical goals. The circuit then facilitates development of a particular plan for a particular patient as a function of the relative importance of the clinical goals. By one approach the control circuit can be configured as a radiation treatment plan recommendation resource that accesses a database of radiation treatment plan formulation content items including at least one of a radiation treatment plan template, an auto-planning algorithm, and an auto-segmentation algorithm.

Abstract: A radiation treatment plan three-dimensional dose prediction machine learning model is trained using a training corpus that includes a plurality of radiation treatment plans that are not specific to a particular patient and wherein the training corpus includes some, but not all, possible patient volumes of interest. Information regarding the patient (including information regarding at least one volume of interest for the patient that was not represented in the training corpus) is input to the radiation treatment plan three-dimensional dose prediction machine model. The latter generates predicted three-dimensional dose distributions that include a predicted three-dimensional dose distribution for the at least one volume of interest that was not represented in the training corpus.

Abstract: Systems and methods for implementing an adaptive therapy workflow that minimizes time needed to create a session patient model, select an appropriate plan for the treatment session, and treat the patient.

Abstract: Systems and methods for the automatic generation and optimization of radiation therapy treatment plans, and systems and methods for the automatic generation and optimization of an adapted plan in an adaptive radiation therapy workflow.

Abstract: Systems and methods for the automatic generation and optimization of radiation therapy treatment plans, and systems and methods for the automatic generation and optimization of an adapted plan in an adaptive radiation therapy workflow.

Abstract: A control circuit accesses historical information regarding previously optimized radiation treatment plans for different patients and processes that information to determine the relative importance of different clinical goals. The circuit then facilitates development of a particular plan for a particular patient as a function of the relative importance of the clinical goals. By one approach the control circuit can be configured as a radiation treatment plan recommendation resource that accesses a database of radiation treatment plan formulation content items including at least one of a radiation treatment plan template, an auto-planning algorithm, and an auto-segmentation algorithm.

Abstract: Systems and methods for implementing an adaptive therapy workflow that minimizes time needed to create a session patient model, select an appropriate plan for the treatment session, and treat the patient.

Abstract: Systems and methods for implementing an adaptive therapy workflow that minimizes time needed to create a session patient model, select an appropriate plan for the treatment session, and treat the patient.